/// <summary>
/// Returns the fitness of the gene
/// </summary>
/// <returns></returns>
public int GetFitness()
{
if (fitness == 0)
{
fitness = FitnessCalculator.GetFitness(this);
}
return(fitness);
}
/**
* Constructs a new population.
* @param size The size of the population.
* @param initialise True, if the population will be initialised.
*/
public Population(FitnessCalculator fitnessCalculator, int size, int bitSize, int genotypeLength, int groupSize, bool initialise = false)
{
candidateSolutions = new CandidateSolution[size];
this.fitnessCalculator = fitnessCalculator;
this.groupSize = groupSize;
if (initialise)
{
for (int i = 0; i < candidateSolutions.Length; i++)
{
CandidateSolution candidateSolution = new CandidateSolution(fitnessCalculator, bitSize, genotypeLength, groupSize);
candidateSolution.GenerateIndividual();
candidateSolutions[i] = candidateSolution;
}
}
}
void RunGA()
{
FitnessCalculator.SetSolution("1111000000000001111000000000000000000000000000000000000000001111");
Population pop = new Population(6, true);
int epoch = 0;
while (pop.GetFittest().GetFitness() < FitnessCalculator.GetMaxFitness())
{
epoch++;
pop = Algorithm.EvolvePopulation(pop);
System.Console.WriteLine("Generation: " + epoch + " Fittest: " + pop.GetFittest().GetFitness());
System.Console.WriteLine(pop.GetFittest());
}
System.Console.WriteLine("Solution found");
System.Console.WriteLine("Generation: " + epoch);
System.Console.WriteLine("Genes:");
System.Console.WriteLine(pop.GetFittest());
}